Omics Sciences and Artificial Intelligence: Future Directions for Tailored Social Medicine

Abstract

Biomedical research is rapidly advancing through the convergence of omics sciences with artificial intelligence (AI) applications. Genomics, transcriptomics, proteomics, and metabolomics, among others, generate multidimensional data that embrace molecular complexity of diseases, whereas AI enables the integration, interpretation, and prediction from these datasets. Together, they contribute to enhance patient-tailored medicine by supporting biomarker discovery, disease classification, patient stratification, and personalized therapies. However, challenges such as data quality, cost, reproducibility, and model interpretability remain. Emerging strategies including federated learning and large language models provide promising solutions, bridging precision and social medicine to promote health equity, improve clinical decision-making, and maximize the societal impact of digital health innovations.